PDF(Pubmed)
Abstract：
BACKGROUND: Synonymous mutations, which change the DNA sequence but not the encoded protein sequence, can affect protein structure and function, mRNA maturation, and mRNA half-lives. The possibility that synonymous mutations might be enriched in cancer has been explored in several recent studies. However, none of these studies control for all three types of mutational heterogeneity (patient, histology, and gene) that are known to affect the accurate identification of non-synonymous cancer-associated genes. Our goal is to adopt the current standard for non-synonymous mutations in an investigation of synonymous mutations.
RESULTS: Here, we create an algorithm, MutSigCVsyn, an adaptation of MutSigCV, to identify cancer-associated genes that are enriched for synonymous mutations based on a non-coding background model that takes into account the mutational heterogeneity across these levels. Using MutSigCVsyn, we first analyzed 2572 cancer whole-genome samples from the Pan-cancer Analysis of Whole Genomes (PCAWG) to identify non-synonymous cancer drivers as a quality control. Indicative of the algorithm accuracy we find that 58.6% of these candidate genes were also found in Cancer Census Gene (CGC) list, and 66.2% were found within the PCAWG cancer driver list. We then applied it to identify 30 putative cancer-associated genes that are enriched for synonymous mutations within the same samples. One of the promising gene candidates is the B cell lymphoma 2 (BCL-2) gene. BCL-2 regulates apoptosis by antagonizing the action of proapoptotic BCL-2 family member proteins. The synonymous mutations in BCL2 are enriched in its anti-apoptotic domain and likely play a role in cancer cell proliferation.
CONCLUSIONS: Our study introduces MutSigCVsyn, an algorithm that accounts for mutational heterogeneity at patient, histology, and gene levels, to identify cancer-associated genes that are enriched for synonymous mutations using whole genome sequencing data. We identified 30 putative candidate genes that will benefit from future experimental studies on the role of synonymous mutations in cancer biology.
RESULTS: Here, we create an algorithm, MutSigCVsyn, an adaptation of MutSigCV, to identify cancer-associated genes that are enriched for synonymous mutations based on a non-coding background model that takes into account the mutational heterogeneity across these levels. Using MutSigCVsyn, we first analyzed 2572 cancer whole-genome samples from the Pan-cancer Analysis of Whole Genomes (PCAWG) to identify non-synonymous cancer drivers as a quality control. Indicative of the algorithm accuracy we find that 58.6% of these candidate genes were also found in Cancer Census Gene (CGC) list, and 66.2% were found within the PCAWG cancer driver list. We then applied it to identify 30 putative cancer-associated genes that are enriched for synonymous mutations within the same samples. One of the promising gene candidates is the B cell lymphoma 2 (BCL-2) gene. BCL-2 regulates apoptosis by antagonizing the action of proapoptotic BCL-2 family member proteins. The synonymous mutations in BCL2 are enriched in its anti-apoptotic domain and likely play a role in cancer cell proliferation.
CONCLUSIONS: Our study introduces MutSigCVsyn, an algorithm that accounts for mutational heterogeneity at patient, histology, and gene levels, to identify cancer-associated genes that are enriched for synonymous mutations using whole genome sequencing data. We identified 30 putative candidate genes that will benefit from future experimental studies on the role of synonymous mutations in cancer biology.
摘要：
背景：同义突变，改变DNA序列，但不改变编码的蛋白质序列，会影响蛋白质的结构和功能，mRNA成熟，和mRNA的半衰期。最近的几项研究已经探索了同义突变可能在癌症中富集的可能性。然而,这些研究都没有控制所有三种类型的突变异质性(患者，组织学,和基因），已知会影响非同义癌症相关基因的准确识别。我们的目标是在同义突变的研究中采用当前的非同义突变标准。
结果：这里，我们创建了一个算法，MutSigCVsyn,改编的MutSigCV，基于非编码背景模型，识别富含同义突变的癌症相关基因，该模型考虑了这些水平上的突变异质性。使用MutSigCVsyn,我们首先分析了来自全基因组泛癌分析(PCAWG)的2572个癌症全基因组样本,以确定非同义癌症驱动因素作为质量控制.表明算法的准确性，我们发现这些候选基因的58.6%也被发现在癌症普查基因（CGC）列表，在PCAWG癌症驱动因素列表中发现了66.2%。然后，我们应用它来鉴定30个推定的癌症相关基因，这些基因在相同的样品中富集了同义突变。有希望的候选基因之一是B细胞淋巴瘤2(BCL-2)基因。BCL-2通过拮抗促凋亡BCL-2家族成员蛋白的作用来调节凋亡。BCL2中的同义突变富集在其抗凋亡结构域中,并且可能在癌细胞增殖中起作用。
结论：我们的研究介绍了MutSigCVsyn，一种解释患者突变异质性的算法，组织学,和基因水平，使用全基因组测序数据鉴定富含同义突变的癌症相关基因。我们确定了30个推定的候选基因，这些基因将受益于有关同义突变在癌症生物学中的作用的未来实验研究。
结果：这里，我们创建了一个算法，MutSigCVsyn,改编的MutSigCV，基于非编码背景模型，识别富含同义突变的癌症相关基因，该模型考虑了这些水平上的突变异质性。使用MutSigCVsyn,我们首先分析了来自全基因组泛癌分析(PCAWG)的2572个癌症全基因组样本,以确定非同义癌症驱动因素作为质量控制.表明算法的准确性，我们发现这些候选基因的58.6%也被发现在癌症普查基因（CGC）列表，在PCAWG癌症驱动因素列表中发现了66.2%。然后，我们应用它来鉴定30个推定的癌症相关基因，这些基因在相同的样品中富集了同义突变。有希望的候选基因之一是B细胞淋巴瘤2(BCL-2)基因。BCL-2通过拮抗促凋亡BCL-2家族成员蛋白的作用来调节凋亡。BCL2中的同义突变富集在其抗凋亡结构域中,并且可能在癌细胞增殖中起作用。
结论：我们的研究介绍了MutSigCVsyn，一种解释患者突变异质性的算法，组织学,和基因水平，使用全基因组测序数据鉴定富含同义突变的癌症相关基因。我们确定了30个推定的候选基因，这些基因将受益于有关同义突变在癌症生物学中的作用的未来实验研究。
